Maximizing Resource Utilization In Video Streaming Systems

Video streaming has recently grown dramatically in popularity over the Internet, Cable TV, and wire-less networks. Because of the resource demanding nature of video streaming applications, maximizing resource utilization in any video streaming system is a key factor to increase the scalability and decrease the cost of the system. Resources to utilize include server bandwidth, network bandwidth, battery life in battery operated devices, and processing time in limited processing power devices. In this work, we propose new techniques to maximize the utilization of video-on-demand (VOD) server resources. In addition to that, we propose new framework to maximize the utilization of the network bandwidth in wireless video streaming systems. Providing video streaming users in a VOD system with expected waiting times enhances their perceived quality-of-service (QoS) and encourages them to wait thereby increasing server utilization by increasing server throughput. In this work, we analyze waiting-time predictability in scalable video streaming. We also propose two prediction schemes and study their effectiveness when applied with various stream merging techniques and scheduling policies. The results demonstrate that the waiting time can be predicted accurately, especially when enhanced cost-based scheduling is applied. The combination of waiting-time prediction and cost-based scheduling leads to outstanding performance benefits. The achieved resource sharing by stream merging depends greatly on how the waiting requests are scheduled for service. Motivated by the development of cost-based scheduling, we investigate its effectiveness in great detail and discuss opportunities for further tunings and enhancements. Additionally, we analyze the effectiveness of incorporating video prediction results into the scheduling decisions. We also study the interaction between scheduling policies and the stream merging techniques and explore new ways for enhancements. The interest in video surveillance systems has grown dramatically during the last decade. Auto-mated video surveillance (AVS) serves as an efficient approach for the realtime detection of threats and for monitoring their progress. Wireless networks in AVS systems have limited available bandwidth that have to be estimated accurately and distributed efficiently. In this research, we develop two cross-layer optimization frameworks that maximize the bandwidth optimization of 802.11 wireless network. We develop a distortion-based cross-layer optimization framework that manages bandwidth in the wire-less network in such a way that minimizes the overall distortion. We also develop an accuracy-based cross-layer optimization framework in which the overall detection accuracy of the computer vision algorithm(s) running in the system is maximized. Both proposed frameworks manage the application rates and transmission opportunities of various video sources based on the dynamic network conditions to achieve their goals. Each framework utilizes a novel online approach for estimating the effective airtime of the network. Moreover, we propose a bandwidth pruning mechanism that can be used with the accuracy-based framework to achieve any desired tradeoff between detection accuracy and power consumption. We demonstrate the effectiveness of the proposed frameworks, including the effective air-time estimation algorithms and the bandwidth pruning mechanism, through extensive experiments using OPNET

802.11s QoS Routing for Telemedicine Service

The merits of 802.11s as the wireless mesh network standard provide a lowcost and high independent scalability telemedicine infrastructure. However,challenges in degradation of performance as hops increase and the absent of Quality of Service (QoS) provision need to be resolved. The reliability and timely manner are the important factor for successful telemedicine service. This research investigates the use of 802.11s for telemedicine services. A new model of 802.11s based telemedicine infrastructure has been developed for this purpose. A non deterministic polynomial path selection is proposed to provide end-to-end QoS provisioning in 802.11s. A multi-metric called QoS Price metric is proposed as measurement of link quality. The QoS Price is derived from multi layers values that reflect telemedicine traffic requirement and the resource availability of the network. The proposed solution has modified the path management of 802.11s and added resource allocation in distributed scheme.DOI:http://dx.doi.org/10.11591/ijece.v4i2.559

Maximizing the stable throughput of heterogeneous nodes under airtime fairness in a CSMA environment

The stability region of non-persistent CSMA is analyzed in a general heterogeneous network, where stations have different mean packet arrival rates, packet transmission times probability distributions and transmission probabilities. The considered model of CSMA captures the behavior of the well known CSMA/CA, at least as far as stability and throughput evaluation are concerned. The analysis is done both with and without collision detection. Given the characterization of the stability region, throughput-optimal transmission probabilities are identified under airtime fairness, establishing asymptotic upper and lower bounds of the maximum achievable stable throughput. The bounds turn out to be insensitive to the probability distribution of packet transmission times. Numerical results highlight that the obtained bounds are tight not only asymptotically, but also for essentially all values of the number of stations. The insight gained leads to the definition of a distributed adaptive algorithm to adjust the transmission probabilities of stations so as to attain the maximum stable throughput

Slicing with guaranteed quality of service in wifi networks

Network slicing has recently been proposed as one of the main enablers for 5G networks. The slicing concept consists of the partition of a physical network into several self-contained logical networks (slices) that can be tailored to offer different functional or performance requirements. In the context of 5G networks, we argue that existing ubiquitous WiFi technology can be exploited to cope with new requirements. Therefore, in this paper, we propose a novel mechanism to implement network slicing in WiFi Access Points. We formulate the resource allocation problem to the different slices as a stochastic optimization problem, where each slice can have bit rate, delay, and capacity requirements. We devise a solution to the problem above using the Lyapunov drift optimization theory, and we develop a novel queuing and scheduling algorithm. We have used MATLAB and Simulink to build a prototype of the proposed solution, whose performance has been evaluated in a typical slicing scenario.This work has been supported in part by the European Commission and the Spanish Government (Fondo Europeo de Desarrollo Regional, FEDER) by means of the EU H2020 NECOS (777067) and ADVICE (TEC2015-71329) projects, respectivel

Orchestrating energy-efficient vRANs: Bayesian learning and experimental results

Virtualized base stations (vBS) can be implemented in diverse commodity platforms and are expected to bring unprecedented operational flexibility and cost efficiency to the next generation of cellular networks. However, their widespread adoption is hampered by their complex configuration options that affect in a non-traditional fashion both their performance and their power consumption requirements. Following an in-depth experimental analysis in a bespoke testbed, we characterize the vBS power cost profile and reveal previously unknown couplings between their various control knobs. Motivated by these findings, we develop a Bayesian learning framework for the orchestration of vBSs and design two novel algorithms: (i) BP-vRAN, which employs online learning to balance the vBS performance and energy consumption, and (ii) SBP-vRAN, which augments our optimization approach with safe controls that maximize performance while respecting hard power constraints. We show that our approaches are data-efficient, i.e., converge an order of magnitude faster than state-of-the-art Deep Reinforcement Learning methods, and achieve optimal performance. We demonstrate the efficacy of these solutions in an experimental prototype using real traffic traces.This work has been supported by the European Commission through Grant No. 101017109 (DAEMON project), and the CERCA Programme/Generalitat de Catalunya